class_docstr: An object that slices tensors into rows or columns as described in the Megatron LM paper
  
  Args:
      mp_group (torch.distributed.ProcessGroupNCCL): Distributed group for model parallelism
docstr: Slice tensors in the column direction.
  
  Args:
      policy_inputs (Tuple): tuple of weight and bias dictionaries
      attributes (Tuple): tuple of weight attributes and bias attributes dictionaries
  
  Returns:
      Tuple: tuple of weights and biases
docstr_tok: ["Slice","tensors","in","the","column","direction",".","Args",":","policy_inputs","(","Tuple",")",":","tuple","of","weight","and","bias","dictionaries","attributes","(","Tuple",")",":","tuple","of","weight","attributes","and","bias","attributes","dictionaries","Returns",":","Tuple",":","tuple","of","weights","and","biases"]
code: def column_slice(
      self,
      policy_inputs: Tuple,
      attributes: Tuple,
  ) -> Tuple:
      """
      Slice tensors in the column direction.
  
      Args:
          policy_inputs (Tuple): tuple of weight and bias dictionaries
          attributes (Tuple): tuple of weight attributes and bias attributes dictionaries
  
      Returns:
          Tuple: tuple of weights and biases
      """
      return self.slice_weight_and_bias(
          policy_inputs,
          attributes=attributes,
          dim=0,
          slice_bias=True,
      )
  
file_path: parallelformers/parallelformers/parallel/slicing.py
imports: import os
  from typing import Dict, Tuple
  import torch
  import torch.distributed
label_desc: Use image node_id 4 for calling the TensorSlicer obj's underlying member method code with example usage: obj.column_slice(policy_inputs, attributes) and returns: self